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Update Column A where Column B has Value C in Pandas DataFrame
Good morning,
I'm trying to update a DataFrame based on the contents of two columns and am running into issues.
Specifically, I have a column called IP, another called VISITTIME. I've added two columns called OLDEST and NEWEST which need to contain the min and max VISITTIME for the IP of that row.
Using:
df2 = pd.merge(df.groupby('IP')['VISITTIME'].min().to_frame(),
df.groupby('IP')['VISITTIME'].max().to_frame(), on="IP")
I can get the min and max times for each IP in the table. I can then iterate over that but I don't know how to update the original DataFrame.
Essentially what I'm asking is how do I do the following in pandas:
UPDATE df SET df.OLDEST = df2.OLDEST, df.NEWEST = df2.NEWEST WHERE df.IP=df2.IP;
I feel like this should be easy and I'm ashamed that it isn't.
Thank you
python pandas dataframe
edited Nov 21 '18 at 19:43
user3471881
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
add a comment |
Good morning,
I'm trying to update a DataFrame based on the contents of two columns and am running into issues.
Specifically, I have a column called IP, another called VISITTIME. I've added two columns called OLDEST and NEWEST which need to contain the min and max VISITTIME for the IP of that row.
Using:
df2 = pd.merge(df.groupby('IP')['VISITTIME'].min().to_frame(),
df.groupby('IP')['VISITTIME'].max().to_frame(), on="IP")
I can get the min and max times for each IP in the table. I can then iterate over that but I don't know how to update the original DataFrame.
Essentially what I'm asking is how do I do the following in pandas:
UPDATE df SET df.OLDEST = df2.OLDEST, df.NEWEST = df2.NEWEST WHERE df.IP=df2.IP;
I feel like this should be easy and I'm ashamed that it isn't.
Thank you
python pandas dataframe
edited Nov 21 '18 at 19:43
user3471881
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
look atpd.update()
– Chris
Nov 21 '18 at 18:40
add a comment |
Good morning,
I'm trying to update a DataFrame based on the contents of two columns and am running into issues.
Specifically, I have a column called IP, another called VISITTIME. I've added two columns called OLDEST and NEWEST which need to contain the min and max VISITTIME for the IP of that row.
Using:
df2 = pd.merge(df.groupby('IP')['VISITTIME'].min().to_frame(),
df.groupby('IP')['VISITTIME'].max().to_frame(), on="IP")
I can get the min and max times for each IP in the table. I can then iterate over that but I don't know how to update the original DataFrame.
Essentially what I'm asking is how do I do the following in pandas:
UPDATE df SET df.OLDEST = df2.OLDEST, df.NEWEST = df2.NEWEST WHERE df.IP=df2.IP;
I feel like this should be easy and I'm ashamed that it isn't.
Thank you
python pandas dataframe
edited Nov 21 '18 at 19:43
user3471881
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
Good morning,
I'm trying to update a DataFrame based on the contents of two columns and am running into issues.
Specifically, I have a column called IP, another called VISITTIME. I've added two columns called OLDEST and NEWEST which need to contain the min and max VISITTIME for the IP of that row.
Using:
df2 = pd.merge(df.groupby('IP')['VISITTIME'].min().to_frame(),
df.groupby('IP')['VISITTIME'].max().to_frame(), on="IP")
I can get the min and max times for each IP in the table. I can then iterate over that but I don't know how to update the original DataFrame.
Essentially what I'm asking is how do I do the following in pandas:
UPDATE df SET df.OLDEST = df2.OLDEST, df.NEWEST = df2.NEWEST WHERE df.IP=df2.IP;
I feel like this should be easy and I'm ashamed that it isn't.
Thank you
python pandas dataframe
python pandas dataframe
edited Nov 21 '18 at 19:43
user3471881
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
edited Nov 21 '18 at 19:43
user3471881
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
edited Nov 21 '18 at 19:43
user3471881
1,1872619
edited Nov 21 '18 at 19:43
user3471881
1,1872619
edited Nov 21 '18 at 19:43
user3471881
1,1872619
1,1872619
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
asked Nov 21 '18 at 18:07
DClarkeDClarke
12
12
Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
look atpd.update()
– Chris
Nov 21 '18 at 18:40
add a comment |
Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
look atpd.update()
– Chris
Nov 21 '18 at 18:40
Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
look at
pd.update()– Chris
Nov 21 '18 at 18:40
look at
pd.update()– Chris
Nov 21 '18 at 18:40
add a comment |
1 Answer
1
active
oldest
votes
Depending on your version of pandas (I know in version > 0.22.0),
there is a method called Dataframe.update.
That should provide some examples, but a few warnings:
- You need to make sure that both dataframes are using IP as an index,
or this method will not work. - The column (Series) names should be the same, so it will not require any location or column referencing.
- You may choose to only overwrite NA values, but not applicable for what you are describing here.
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
Depending on your version of pandas (I know in version > 0.22.0),
there is a method called Dataframe.update.
That should provide some examples, but a few warnings:
- You need to make sure that both dataframes are using IP as an index,
or this method will not work. - The column (Series) names should be the same, so it will not require any location or column referencing.
- You may choose to only overwrite NA values, but not applicable for what you are describing here.
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
add a comment |
Depending on your version of pandas (I know in version > 0.22.0),
there is a method called Dataframe.update.
That should provide some examples, but a few warnings:
- You need to make sure that both dataframes are using IP as an index,
or this method will not work. - The column (Series) names should be the same, so it will not require any location or column referencing.
- You may choose to only overwrite NA values, but not applicable for what you are describing here.
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
add a comment |
Depending on your version of pandas (I know in version > 0.22.0),
there is a method called Dataframe.update.
That should provide some examples, but a few warnings:
- You need to make sure that both dataframes are using IP as an index,
or this method will not work. - The column (Series) names should be the same, so it will not require any location or column referencing.
- You may choose to only overwrite NA values, but not applicable for what you are describing here.
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
Depending on your version of pandas (I know in version > 0.22.0),
there is a method called Dataframe.update.
That should provide some examples, but a few warnings:
- You need to make sure that both dataframes are using IP as an index,
or this method will not work. - The column (Series) names should be the same, so it will not require any location or column referencing.
- You may choose to only overwrite NA values, but not applicable for what you are describing here.
DataFrame.update(other, join='left', overwrite=True, filter_func=None, raise_conflict=False)
Modify in place using non-NA values from another DataFrame.
Aligns on indices. There is no return value.
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
answered Nov 21 '18 at 18:44
rogersdevoprogersdevop
5613
5613
add a comment |
add a comment |
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Please add an example of input data and expected output.
– user3471881
Nov 21 '18 at 18:15
See: How to make good reproducible pandas examples
– root
Nov 21 '18 at 18:17
look at
pd.update()– Chris
Nov 21 '18 at 18:40